"""simple docstring"""



import unittest

from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
@require_sentencepiece
@slow  # see https://github.com/huggingface/transformers/issues/11457
class  snake_case__      (       snake_case_,						unittest.TestCase       ):
 _snake_case       :					int							  =						BarthezTokenizer
 _snake_case       :					List[Any]							  =						BarthezTokenizerFast
 _snake_case       :					Tuple							  =						True
 _snake_case       :					str							  =						True


 def 						a__   ( self						):
        super().setUp()

        __a       =    BarthezTokenizerFast.from_pretrained("moussaKam/mbarthez"						)
        tokenizer.save_pretrained(self.tmpdirname						)
        tokenizer.save_pretrained(self.tmpdirname    ,		legacy_format=lowerCamelCase						)
        __a       =    tokenizer


 def 						a__   ( self						):
        __a       =    "<pad>"
        __a       =    1

        self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowerCamelCase						)    ,		lowerCamelCase						)
        self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowerCamelCase						)    ,		lowerCamelCase						)


 def 						a__   ( self						):
        __a       =    list(self.get_tokenizer().get_vocab().keys()						)

        self.assertEqual(vocab_keys[0]    ,		"<s>"						)
        self.assertEqual(vocab_keys[1]    ,		"<pad>"						)
        self.assertEqual(vocab_keys[-1]    ,		"<mask>"						)
        self.assertEqual(len(lowerCamelCase						)    ,		101122						)


 def 						a__   ( self						):
        self.assertEqual(self.get_tokenizer().vocab_size    ,		101122						)


 @require_torch
 def 						a__   ( self						):
        __a       =    ["A long paragraph for summarization.", "Another paragraph for summarization."]
        __a       =    [0, 57, 3018, 70307, 91, 2]

        __a       =    self.tokenizer(
            lowerCamelCase    ,		max_length=len(lowerCamelCase						)    ,		padding=lowerCamelCase    ,		truncation=lowerCamelCase    ,		return_tensors="pt"						)
        self.assertIsInstance(lowerCamelCase    ,		lowerCamelCase						)

        self.assertEqual((2, 6)    ,		batch.input_ids.shape						)
        self.assertEqual((2, 6)    ,		batch.attention_mask.shape						)
        __a       =    batch.input_ids.tolist()[0]
        self.assertListEqual(lowerCamelCase    ,		lowerCamelCase						)


 def 						a__   ( self						):
        if not self.test_rust_tokenizer:
               return

        __a       =    self.get_tokenizer()
        __a       =    self.get_rust_tokenizer()

        __a       =    "I was born in 92000, and this is falsé."

        __a       =    tokenizer.tokenize(lowerCamelCase						)
        __a       =    rust_tokenizer.tokenize(lowerCamelCase						)
        self.assertListEqual(lowerCamelCase    ,		lowerCamelCase						)

        __a       =    tokenizer.encode(lowerCamelCase    ,		add_special_tokens=lowerCamelCase						)
        __a       =    rust_tokenizer.encode(lowerCamelCase    ,		add_special_tokens=lowerCamelCase						)
        self.assertListEqual(lowerCamelCase    ,		lowerCamelCase						)

        __a       =    self.get_rust_tokenizer()
        __a       =    tokenizer.encode(lowerCamelCase						)
        __a       =    rust_tokenizer.encode(lowerCamelCase						)
        self.assertListEqual(lowerCamelCase    ,		lowerCamelCase						)






 @slow
 def 						a__   ( self						):
        # fmt: off
        __a       =    {"input_ids": [[0, 490, 14328, 4507, 354, 47, 43669, 95, 25, 78117, 20215, 19779, 190, 22, 400, 4, 35343, 80310, 603, 86, 24937, 105, 33438, 94762, 196, 39642, 7, 15, 15933, 173, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 10534, 87, 25, 66, 3358, 196, 55289, 8, 82961, 81, 2204, 75203, 7, 15, 763, 12956, 216, 178, 14328, 9595, 1377, 69693, 7, 448, 71021, 196, 18106, 1437, 13974, 108, 9083, 4, 49315, 7, 39, 86, 1326, 2793, 46333, 4, 448, 196, 74588, 7, 49315, 7, 39, 21, 822, 38470, 74, 21, 66723, 62480, 8, 22050, 5, 2]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
        # fmt: on

        # moussaKam/mbarthez is a french model. So we also use french texts.
        __a       =    [
            "Le transformeur est un modèle d'apprentissage profond introduit en 2017, "
            "utilisé principalement dans le domaine du traitement automatique des langues (TAL).",
            "À l'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus "
            "pour gérer des données séquentielles, telles que le langage naturel, pour des tâches "
            "telles que la traduction et la synthèse de texte.",
        ]

        self.tokenizer_integration_test_util(
            expected_encoding=lowerCamelCase    ,		model_name="moussaKam/mbarthez"    ,		revision="c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6"    ,		sequences=lowerCamelCase    ,		)

"""simple docstring"""



def 	_lowerCamelCase(	a = 1   ,    a = 1_0_0_0				):
       __a       =    1
       __a       =    0

       for divide_by_number in range(a   ,    digit + 1				):
              __a       =    []
              __a       =    numerator
              for _ in range(1   ,    digit + 1				):
                     if now_divide in has_been_divided:
                            if longest_list_length < len(a				):
                                   __a       =    len(a				)
                                   __a       =    divide_by_number
                     else:
                            has_been_divided.append(a				)
                            __a       =    now_divide * 1_0 % divide_by_number

       return the_digit


# Tests
if __name__ == "__main__":
   import doctest

   doctest.testmod()
